🍄Model Inference (experimental)

Example showing how to use the models inference functionality.

The Bubbaloop server is able to run inference efficiently Visual Language Models (VLM) using directly the camera streams without any latency in the same process and broadcast the results.

Supported models (via Kornia / Candle)

• PaliGemma: https://ai.google.dev/gemma/docs/paligemma

Edit the pipeline

Similar to the Camera Recording pipeline, we can customize the inference.ron pipeline to adjust to our system setup. This will require compiling every time you modify your config.

(
    tasks: [
        // NOTE: Modify this block to customize
        (
            id: "cam0",
            type: "crate::cu29::tasks::VideoCapture",
            config: {
                config: {
                // URL of the RTSP camera
                "source_type": "rtsp",
                "source_uri": "rtsp://<username>:<password>@<ip>:<port>/<stream>"
            }
        ),
        (
            id: "inference",
            type: "crate::cu29::tasks::Inference",
        ),
        (
            id: "bcast_text",
            type: "crate::cu29::tasks::BroadcastChat",
        ),
        (
            id: "bcast_image",
            type: "crate::cu29::tasks::BroadcastImage",
        ),
    ],
    cnx: [
        (src: "cam0", dst: "inference", msg: "crate::cu29::msgs::ImageRgb8Msg"),
        (src: "cam0", dst: "bcast_image", msg: "crate::cu29::msgs::ImageRgb8Msg"),
        (src: "inference", dst: "bcast_text", msg: "crate::cu29::msgs::PromptResponseMsg"),
    ],
    logging: (
        slab_size_mib: 1024, // Preallocates 1GiB of memory map file at a time
        section_size_mib: 100, // Preallocates 100MiB of memory map per section for the main logger.
        enable_task_logging: false,
    ),
)

Start the server

just serve

Start the inference

just start-pipeline inference 0.0.0.0 3000

By default, this command will start the inference engine using the prompt "cap en" — to generate a short capture from each frame.

Check the supported prompts: https://ai.google.dev/gemma/docs/paligemma/prompt-system-instructions

In your terminal you should be able to get somethin similar

[2025-04-06T14:20:13Z INFO  bubbaloop::api::server] 🚀 Starting the server
[2025-04-06T14:20:13Z INFO  bubbaloop::api::server] 🔥 Listening on: 0.0.0.0:3000
[2025-04-06T14:20:13Z INFO  bubbaloop::api::server] 🔧 Press Ctrl+C to stop the server
[2025-04-06T14:20:31Z DEBUG bubbaloop::cu29::tasks::inference] Received response from inference thread: PromptResponseMsg { prompt: "cap en", response: " Two people are sitting on the bed. In-front of them there is a table with some objects and other things on it. On top of them there is roof, light and we can see trees and sky in the background is sunny." }

Inference settings

We expose some setting via a REST api to the following end point.

curl -X POST "http://localhost:3000/api/v0/inference/settings" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "answer Is there any human?"}'

This will fix the prompt to run inference on to detect people

Broadcast

You can access also to the image streams and prompts results via the following API including their timestamps.

Jpeg encoded images

http://localhost:3000/api/v0/streaming/image

Model inference results

http://localhost:3000/api/v0/inference/results

Visualize streams with inference results

We provide a small Python script that calls the above end points and visualize the results with Rerun

https://github.com/kornia/bubbaloop/blob/main/examples/python-inference/client.py

Stop inference

To stop the pipeline, use the stop-pipeline command:

just stop-pipeline inference 0.0.0.0 3000